Q: Using at least 3 biological replicates in an experiment is recommended to do. What do you suggest: At which step of calculation of the relative

Transcription

1 The questions below have been asked by attendees of the qpcr webinar series, Part 2: Analyzing Your Data. All the questions, including the questions that could not be answered during the webinar have been answered below by Robert Sjöback, Ph.D., Tataa Biocenter. Table of content Q1: I have a question about the reference gene. I am working with plant-pathogen interactions and in each cdna sample there are two different organisms. How can I choose the best reference gene for relative quantification for normalization the plant genes or the pathogen genes? During a time course experiment the amount of pathogen is increasing with the time and the amount of plant is decreasing... 3 Q2: Is it a good idea to normalize based on RNA quantity or number of cells used in the experiment, rather than using a specific reference gene?... 4 Q3: How the calculated qpcr efficiency affects the final results? Below which value should the efficiency be considered too low for producing accurate results?... 4 Q4: Do you have to use an RNA standard prepared each time you need it?... 4 Q5: How do you validate the stability of the reference gene?... 5 Q6: How is the qpcr efficiency determined?... 5 Q7: How many genes should be in the study before global mean normalization is feasible (as compared to ref. gene normalization)?... 5 Q8: At what level are replicates important to perform on a routine basis (biological/ RT level/ PCR level) - where is the greatest variance introduced?... 5 Q9: what in your opinion is the "golden standard" to determine the qpcr assay efficiency - running serial dilutions and calculating the efficiency from the slope, or by analyzing the individual amplification plot shape (for example by LinRegPCR)?... 6 Q10: During my work with mirnas, I have used mir-16 as an endogenous control but not RNU6B (common endogenous control), what do you think about this? Kindly note that I had a good stability with mir Q11: When I check the efficiency of my oligos with a standard curve on genomic DNA and the difference of efficiency between the different targets and my housekeeping gene is less than 10% can I use these primers in a delta delta CT method on cdna?... 6 Q12: Could you explain more about qpcr validation and how to do it? What are the parameters in validation, the acceptance criteria in molecular validation? For example: in my research, how if I want to validate my method about detection pathogen bacteria in food by using real time PCR Q13: For absolute quantification, you need a standard curve for every single gene if efficiency is different between genes?... 7 Q14: If you want to determine cdna levels from genes in animal heart tissue. Would you prefer the absolute quantification method or the relative quantification method?... 7 Q15: How should we compare deltadeltact data from different runs? How can we make sure that the reactions effectiveness is the same in each run?... 8

2 Q: Using at least 3 biological replicates in an experiment is recommended to do. What do you suggest: At which step of calculation of the relative expression should the averaging of the values of that replicates be done (e.g. at the level of ct-values or at the level of fold changes)? Because the standard error may vary much - depending on the level at which you've done the averaging of the replicates. (see also the 1 st webinar Starting with qpcr )... 8 Q: Should I include all of my genes in the GeNorm analysis when determining reference genes to use? Or just the genes I expect will be appropriate for use as ref genes?... 8 Q: what means the appearance of the negative Ct values?... 9 Q: Can you tell a bit more about how to detect for inhibition in your samples, for example SPUDassay and give an example of a partial inhibitor. I used isopropanol, fenol and DNAse inactivation reagent but always got total inhibition Q: should we repeat the same plate analysis two times?... 9 Q: Is it a panel of genes to be tested when working with bacteria? Can we use GeNorm or Normfinder to analyse the data?... 9 Q: I have a question about normalisation factor. I know ideally normalisation factor should be equal in all samples...but in my case the normalisation factor in my samples do vary...i always use equal quantity of RNA as starting point. Is it ok to report such variation in NF value? I am using 8 reference genes...my genorm value is between I am using qbase software. According to this software genorm value should be ideally less than but my genorm values are close to is it acceptable? Can I use the three most stable reference genes??... 9 Q: How to calculate fold change expression in relative quantification, when a given gene is not expressed in one of the samples?... 9 Q: What do you think about efficiency calculation for each well (separatly)? Q: When using qpcr for determination of transgene copy number in a transgenic mouse what is the best standard, the purified plasmid carrying the transgene or an endogenous gene with known copy number? Q: I am analyzing the bacterial composition from intestinal sample using a setup with 24 different primer sets targeting 16S rrna genes of bacterial groups of different phylogenetic levels. I would like to use relative quantification, but I am unsure of how I should normalize my samples. My samples are of so low concentrations that it is impossible to get good standard curves. Can I circumvent this? Q: What do you prefer: running plasmid as standards for absolute quantification or using serial dilution of cdna for relative quantification? Q: What are the best softwares in your opinion to analyse results? Are the software supplied with the instruments sufficient? Q: Is it important that we use 16S rrna in bacteria as a reference or we can use another reference? Q: Can rrna be safely used as reference gene because the amount of rrna is huge compared to genes of interest: my Cq for 16S is 6-7 and Cqs for genes of interest are between Can I use more diluted sample for reference runs to get similar Cqs with 16S as with genes of interest?... 11

3 Q: when analyzing qpcr data with delta delta Ct, which is the most appropriate statistical test we must use and when applying standard deviation, which data we use for that? (row Ct or delta Ct or normalized delta delta Ct)? Q: Are there recommendations for the number of replicates in RT qpcr: of course biological replicates (i.e. 3) are needed but how about RT replicates, qpcr replicates (within a run/ in a separate run)? Q: Are both derivative method and fit point analysis the same in the result or are there any advantages of using one over the other? Q: how many ways to calculate the deltadeltact and which of them is the most relatively acceptable? Q: TaqMan oligos and probes are supposed to achieve 100% efficiency in qpcr assays. Do you still perform standard curves when using TaqMan probes? Q: I would like to know how long it takes to validate qrt-pcr reaction and to "get" proper efficiency of reaction? Q: (Additional question from the person analyzing pathogens in plants) thanks a lot for the answer, but it is still not clear because using GeNorm or Normfinder, both programs calculate the best ref gene based on constant expression of the gene within the samples, but due changes in the amount of plant or pathogen in each sample, the gene expression looks not constant because the amount of plant or pathogen is different. I would like to know the pathogen gene expression (using as a ref gene a pathogen gene) and also plant pathogen gene expression (using as a ref a plant gene) Q: Is it possible to use double normalization, 1 to normalize for tissue type (when sample contains mixed tissue types) and second to normalize to ref genes? Q: We observe that the exponential growth of the PCR curve often does not correlate as expected with assay efficiency. Ie a steeper curve in the exponential phase doesn't necessarily mean that the assay has high efficiency (from standard curves ). Have you observed the same? And if so, can you explain the reason for this? Q1: I have a question about the reference gene. I am working with plant-pathogen interactions and in each cdna sample there are two different organisms. How can I choose the best reference gene for relative quantification for normalization the plant genes or the pathogen genes? During a time course experiment the amount of pathogen is increasing with the time and the amount of plant is decreasing A: That is a rather difficult situation, and it depends on what answers you want. If you would like to quantify the number of pathogens in the plants, you should choose a reference gene that is constantly expressed in the plant during the different amount of pathogens presence. Then you would quantify the number of pathogens in the plant material. If you want to see how the gene expression in the plant changes depending on how much pathogens you have present, you would need to choose a reference gene in the plant system that is constantly expressed at different levels

4 of pathogen and that is not present in the pathogen itself. If you want to quantify both how the gene expression changes in the plant and in the bacteria, you would need to work with two different systems: one for the plant where you have the reference gene present and constantly expressed in the plants and another system for the pathogen where you have a reference gene in the pathogen that doesn t change over the time course of your study, but it s a rather complicated system and it depends on what you are looking for and on your set up. Q2: Is it a good idea to normalize based on RNA quantity or number of cells used in the experiment, rather than using a specific reference gene? That depends on your setup. If you re using total RNA as a normalizer, you will not take differences in RT yield or the quality of RNA into account. It s rather easy to check if the total RNA is a good normalizer. When you do your normal evaluation of reference gene, you just add a column of constant values in your analysis, because if your total RNA is a good normalizer, then when you do the analysis in genorm or Normfinder, this should come out as a good option for reference. Normally, it does if you have good quality of RNA. If your RNA is more degraded, the reference gene is usually much better. When it comes to the total number of cells, it depends on the quality of your material. If you think about the gene expression analysis experiment you do, you normally want to look at the expression of a specific gene per cell so if you can quantify the number of cells, then you automatically get the number of copies per cell. However, in this case you are not taking RT yield, RNA quality or extraction efficiency into account. Q3: How the calculated qpcr efficiency affects the final results? Below which value should the efficiency be considered too low for producing accurate results? The problem in this case is not really the absolute value of the qpcr efficiency! The problem is if the efficiency changes between samples. As long as you have a constant efficiency for all your samples, the absolute value of the efficiency has a minor effect on the quantification result. However, if you have a very low efficiency, it indicates that you already have something that is disturbing your system, something that reduces the efficiency of your assay. It indicates that it could be easier to further disturb the system, which means that it might be difficult to get the same efficiency for the different samples. And if you have a different efficiency for different samples, especially for your reference genes and your genes of interest, it can have a large effect on the quantification results. Many people feel that if you are below approx 80% efficiency, you should try to do something to improve the efficiency. That could be a rough guideline, but it depends more on if your efficiency varies between samples, than the absolute value. Q4: Do you have to use an RNA standard prepared each time you need it? There are a few options. You make a preparation of your RNA standard and you include it in your system. What you can do is to use the RNA standard, if you have a good quality well quantified RNA standard to determine the RT efficiency and if you have been able to determine the RT efficiency properly, you could use a DNA standard and determine the amount of cdna. Then you can use the efficiency to calculate the amount of RNA in your sample. Normally we assume that the RT efficiency is constant at least as long as we can make sure that we do not have any inhibition of the RT step and

5 also that we have reasonably the same amount of RNA in the reverse transcription. That s an alternative. Otherwise I d say that you need your RNA standard especially for one step RT PCR. Q5: How do you validate the stability of the reference gene? What you normally do is to you make sure that you have a representative set of samples from your study. That means that if you have a treated and an untreated group, you have a certain amount of samples in both the treated and the untreated group, then you have a number of potential reference genes, usually around 10 to 12 genes, and you run the potential reference genes on these representative samples. Then you re using one of the assigned algorithms like GeNorm and Normfinder and you evaluate the outcome, and they will give you which of these genes are most stably expressed in these representative samples. Q6: How is the qpcr efficiency determined? The most common way to determine the efficiency is to make the dilution series. If you determine the efficiency of your assay as such, you normally use a standard or a PCR product and you make the dilution series. Then you plot the Cq values against the logarithms of the concentrations and fit the straight lines of that. From the slope of that standard curve, you can calculate the efficiency as 10-1/Slope -1. There is also software that tries to estimate the efficiency from the slope of the amplification curve. They are in general not that specific so far. If you want to determine the efficiency of a specific assay in a specific sample, you have to take that specific sample and make the dilution series of that sample and make the same as above: plot the Cq values against the logarithms of the concentration, fit the straight lines and calculate the efficiency from the slope. Q7: How many genes should be in the study before global mean normalization is feasible (as compared to ref. gene normalization)? The global mean normalization is assuming that you have sufficient genes, so that the amount of upregulated genes and down-regulated genes even out. Today I would say that the most common time to use global mean normalization is when analyzing microrna and using the panels available where you analyze most of the known mirna, usually some 700 targets. If you re working with mrna, it depends on your setup because normally, when you re working with mrna, you have a very limited amount of genes that already have been selected to be up- or down-regulated. The other problem is that if you want to compare different studies, you have to work with the same amount of genes, and with the same genes to get the same baseline and make them comparable. So normally it s quite OK if you have a limited study to work with the global mean normalization. If you try to compare different studies, then you have to make sure you have proper normalizations and baselines. How many you need will depend completely on your set-up and which genes are chosen, but normally I would say at least over a hundred, probably even more to get something that is reasonably robust. Q8: At what level are replicates important to perform on a routine basis (biological/ RT level/ PCR level) - where is the greatest variance introduced? Normally, it s always important to have the biological replicates (see also webinar 1: Starting with qpcr). What you want to see in most studies is how much the biological variance is compared to some kind of treatment. Then you have to have biological replicates to be able to tell anything about

6 the statistical significant difference in these cases. In a normal study, people are working very often with a number of replicates on the PCR step. Unless you have very high Cq values, at least above in most case, the qpcr step is normally very reproducible. In the normal case, the reverse transcription is more variable than the qpcr step, and the extraction is usually more variable than the RT step, and what you want to analyze in the end is the biological variation. It s normally recommended to start by making a pilot study (described in webinar 1: Starting with qpcr), so that you could evaluate in which steps you have the largest variance and then you would like to place your replicates at this step. Of course it s also more expensive to place the replicates at the higher level in your analysis. So there has to be some kind of compromise. But you need to have replicates at the biological level to be able to tell a difference between for example treated and untreated group, but you also want to make sure that you have a low technical variation, otherwise it will make it more difficult to see the biological variation between the groups. If you want to draw any statistical conclusion, you have to work with replicates, also at the biological level. Q9: what in your opinion is the "golden standard" to determine the qpcr assay efficiency - running serial dilutions and calculating the efficiency from the slope, or by analyzing the individual amplification plot shape (for example by LinRegPCR)? The most common is to use serial dilutions to determine the efficiency. You probably want to make that in combination with some kind of test for inhibition, especially if you re trying to analyze the efficiency in a specific sample. You could have inhibition from the sample that really perturbs the efficiency determination, but I would say using dilution series is most common today. Looking at the slope of the amplification curve is most commonly used to make sure that you have the same efficiency in each sample, and to look at the slope of the different amplification curves to see if there is a specific sample that has a significantly different slope from the others. Then you should be careful when analyzing that sample together with the others because you have an indication that you have a different efficiency in this sample. Q10: During my work with mirnas, I have used mir-16 as an endogenous control but not RNU6B (common endogenous control), what do you think about this? Kindly note that I had a good stability with mir-16. I would recommend also for microrna to make an evaluation similar to the mrna so that you select a number of potential reference micrornas and run those on a representative set of samples. Then you would do the analysis very much the same way. It s very difficult to say anything about the specific microrna for a specific study because like for mrna, it s very difficult to think that you could have one specific microrna that would be constant in all kinds of experiments. You should really evaluate the amount of microrna or reference microrna in the same way as you do for your mrna. Q11: When I check the efficiency of my oligos with a standard curve on genomic DNA and the difference of efficiency between the different targets and my housekeeping gene is less than 10% can I use these primers in a delta delta CT method on cdna? It s difficult to give a general answer, but normally, I d say yes, unless you re trying to look for up- and down-regulations that are very small. Then, you could introduce errors. Again, it s more important that you make sure that you have the same efficiency for each sample. The absolute value is not

7 really introducing a very large error. The other thing is that by determining the efficiency, unless you have a standard curve with a lot of replicates that covers a large range, it s associated with some errors. So if you have differences that you measure, unless you repeat the standard curve several times and take some kind of average, you may have rather similar efficiencies and it may be difficult to determine efficiency in a precise way. So I would say yes, if you have less than 10% difference between your different genes, it usually works well to work the ΔΔCq. Q12: Could you explain more about qpcr validation and how to do it? What are the parameters in validation, the acceptance criteria in molecular validation? For example: in my research, how if I want to validate my method about detection pathogen bacteria in food by using real time PCR. First of all, you need to evaluate the specificity of the assay. The normal way to do that is that you have a certain number of related species that you test your assay on, so that you do not have any cross reactions. Of course, you do that when you do the design in silico, but normally you want to do it experimentally as well. Then you need to have some kind of standard to compare with. If you have a standard of known concentration, you should be able to quantify that with the method, because normally you would need some kind of standard in your standard curve, but also samples of known concentration. Or you would have to be able to quantify the unknown samples with some golden standard method and compare your result. Then you would want to determine the accuracy of the assays and the repeatability or reproducibility. What you normally do is run a certain number of replicates on the biological level and take those through all steps, including the potential extraction, if it s an RNA target, you have the RT step, and then you have the PCR step and look at the reproducibility of these assays. You would also like to determine the linearity, the limit of detection and the limit of quantification for the assays. It s quite extensive, so it would be difficult to go through all the steps here. Q13: For absolute quantification, you need a standard curve for every single gene if efficiency is different between genes? Yes, you normally need to do one for each target. The reason is that each assay gives rise to a different fluorescent signal for a certain number of molecules. The easiest way to think of this is if you have a SYBR Green experiment, and if you have 2 different assays, one that has a twice as long amplicon as the other, then you need to accumulate only approx half of that amount of molecules to reach the same fluorescence level and to reach the same threshold level. So you can t really compare different genes directly, because they will need to accumulate different amounts of molecules to reach the same threshold level. So you would need to have a separate standard curve for each of your targets. It s not only the efficiency that differs between them. Q14: If you want to determine cdna levels from genes in animal heart tissue. Would you prefer the absolute quantification method or the relative quantification method? It depends on what you want to do. If you only want to look at differences between different animals, you would use the relative quantification. If you would need some kind of absolute value for each specific animal, you would work with the absolute quantification method. Normally, when you do this kind of studies, you have perhaps treated vs untreated group, and then you normally compare them with relative quantification. In some applications, you want to get the absolute

8 values, and then you need to have a different setup and you need to work with the absolute quantification. Q15: How should we compare deltadeltact data from different runs? How can we make sure that the reactions effectiveness is the same in each run? Remember that when you are using the Cq method you are assuming that you have the same efficiency for all genes in all samples. If you have different efficiencies for different samples you are always in trouble no matter if you run your samples on the same or different plates. What you normally try to do if you need to divide your runs on different plates, is that you either try to run all the samples for a specific gene on one plate, if that s not possible you try to run all the genes for a specific sample on the same plate. In that case, if you work with the ΔΔCq the systematic differences you have will cancel. If you are in the situation where you have to split your study in a way that you can t run either all samples or all genes on the same plate, you will have to include an interplate calibrator, that is a sample that is very robust and an assay that is very robust, that you run on each of the plates and then you need to normalize so that you get the same Cq values for each plate. But if you can run either all samples or all genes for a specific sample on one plate, and work with the ΔΔCq equation, the systematic differences are cancelling in the calculation. Additional questions (as they came): Q: Using at least 3 biological replicates in an experiment is recommended to do. What do you suggest: At which step of calculation of the relative expression should the averaging of the values of that replicates be done (e.g. at the level of ct-values or at the level of fold changes)? Because the standard error may vary much - depending on the level at which you've done the averaging of the replicates. (see also the 1 st webinar Starting with qpcr ) When you should average the replicates depend on your experimental set-up. For example, if you perform a reverse transcription (RT) reaction in duplicate followed by qpcr in duplicate in a singleplex reaction (for example by SYBR) you need to average the qpcr replicates before you do the normalization with the reference gene (RG), then normalize with the reference gene, and finally average the RT, because in this case you cannot know which the Cq-values for your gene of interest (GOI) is connected to which Cq-value for the RG. However, if you have performed the qpcr reaction as a duplex reaction with GOI and RG amplified in the same well you need to do the normalization with reference gene before you average the qpcr replicates because now the Cq-value for the GOI and RG for each well is connected. Similarly you would have to analyse your set-up for the other steps of your analysis. Q: Should I include all of my genes in the GeNorm analysis when determining reference genes to use? Or just the genes I expect will be appropriate for use as ref genes? Preferably you should include only the genes that you are assuming to be potential reference genes.

9 Q: what means the appearance of the negative Ct values? If you do not get a Cq-value it means that you either have an inhibited sample, or have no or lower amount than your limit of detection in that particular PCR tube. Q: Can you tell a bit more about how to detect for inhibition in your samples, for example SPUDassay and give an example of a partial inhibitor. I used isopropanol, fenol and DNAse inactivation reagent but always got total inhibition. For the SPUD assay I think it is better to read the original paper: Nolan et al. Anal. Biochem. 351 (2006) If you want to have a partial inhibition either isopropanol or phenol should work if present in low enough concentrations. For demonstration purposes we sometimes use 2.5% ethanol (final concentration) in the PCR reaction. Q: should we repeat the same plate analysis two times? It is always recommended to work with replicates to be able to tell anything about reproducibility and precision of your results. Q: Is it a panel of genes to be tested when working with bacteria? Can we use GeNorm or Normfinder to analyse the data? I am not aware of any commercially available kit for reference gene selection for bacteria, but the procedure would be the same as described above using GeNorm and Normfinder. Q: I have a question about normalisation factor. I know ideally normalisation factor should be equal in all samples...but in my case the normalisation factor in my samples do vary...i always use equal quantity of RNA as starting point. Is it ok to report such variation in NF value? I am using 8 reference genes...my genorm value is between I am using qbase software. According to this software genorm value should be ideally less than but my genorm values are close to is it acceptable? Can I use the three most stable reference genes?? Even though 0.5 seem to be a reasonable guideline, what M-value is acceptable or not is not a fixed value. What is important to remember is that the higher the M-value the more variation is introduced into your data from the reference genes. So the higher the M-value the higher difference is required between the groups you analyze to be able to say that the difference is significant. However, if you get M-values over 0.5 you should also evaluate your data and set-up. If you don t have problem with high Cq-values (low amount of material), poor RNA quality and/or poor selection of candidate reference genes it should not be a problem to get a M-value well below 0.5. Q: How to calculate fold change expression in relative quantification, when a given gene is not expressed in one of the samples? This is a very difficult situation and first of all I would like to say that if you have a large number of missing data you should be very careful when drawing any conclusions from the experiment. Formally if you have missing data, your data set is not normal distributed anymore and you should work with non-parametric statistical tests that don t assume any specific distribution. However, for practical purposes you normally try to replace the missing data point with a reasonable value.

10 What value to use depends on what you think is the reason for having a missing data; is it because there is a technical error in that well or is it because you have so little material that the amount in that well is below your limit of detection. In the first case you should try to replace the data with information from replicate measurements. In the second case a common way is to replace the missing data points with the maximum Cq-value measured for that particular assay plus a constant. How large this constant should be depends on whether you think that the amount in the sample is just above the limit of detection or if the target is not present at all in that sample. In the first case the constant should be small, perhaps one, and in the second case larger, perhaps four. However, it is important to remember that when you do these adjustments of the data you are making assumptions and you potentially introduce errors in your data and your conclusions. Q: What do you think about efficiency calculation for each well (separatly)? I have not done any extensive evaluation of different methods, but with my limited experience I have not found it to be very reliable and therefore avoided using that. Q: When using qpcr for determination of transgene copy number in a transgenic mouse what is the best standard, the purified plasmid carrying the transgene or an endogenous gene with known copy number? I think it s best to use two standards curves; one based on plasmid carrying the transgene and another based on genomic DNA (or plasmid) for a gene of known copy number. (See also the Q&A from 1 st webinar Starting with qpcr ) Q: I am analyzing the bacterial composition from intestinal sample using a setup with 24 different primer sets targeting 16S rrna genes of bacterial groups of different phylogenetic levels. I would like to use relative quantification, but I am unsure of how I should normalize my samples. My samples are of so low concentrations that it is impossible to get good standard curves. Can I circumvent this? I cannot answer this question without knowing more about the set-up and the question you would like to answer with your experiments. Q: What do you prefer: running plasmid as standards for absolute quantification or using serial dilution of cdna for relative quantification? See question 14 above. Q: What are the best softwares in your opinion to analyse results? Are the software supplied with the instruments sufficient? We are working more or less exclusively with the GenEx software ( The instrument software are normally very limited in what they can do and are sufficient only for some of the most basic applications.

11 Q: Is it important that we use 16S rrna in bacteria as a reference or we can use another reference? I will do qpcr with 48 E.coli genes with 3 reference genes. I will use 16S ribosomal RNA. Some say that it s not a good reference gene. Please tell me what should be the criteria of chosing a good reference gene. I would suggest that you make a validation of the most proper reference genes using the method described above. It is no guarantee that 16S is the best or even a good reference gene for your particular set-up unless you have validated that. Q: Can rrna be safely used as reference gene because the amount of rrna is huge compared to genes of interest: my Cq for 16S is 6-7 and Cqs for genes of interest are between Can I use more diluted sample for reference runs to get similar Cqs with 16S as with genes of interest? Have you validated that 16S is a good reference in your particular set-up? If not I would recommend that you do so using the method described above. You can dilute your sample between measuring your genes of interest and the reference gene, but remember that you are introducing further variation into your result due to the additional handling step. Q: when analyzing qpcr data with delta delta Ct, which is the most appropriate statistical test we must use and when applying standard deviation, which data we use for that? (row Ct or delta Ct or normalized delta delta Ct)? It depends on your experimental set-up. For example, are you comparing groups, are there two or more groups, is it a time-course study, is it a dose-response study, are the data normal distributed, etc.? Q: Are there recommendations for the number of replicates in RT qpcr: of course biological replicates (i.e. 3) are needed but how about RT replicates, qpcr replicates (within a run/ in a separate run)? See question above. Q: Are both derivative method and fit point analysis the same in the result or are there any advantages of using one over the other? Both methods normally work well. When you have good, nice looking amplification curves the second derivative method often give higher reproducibility of replicates, but for more troublesome samples it can sometimes be easier to get a consistent result with the fit point method. Q: how many ways to calculate the deltadeltact and which of them is the most relatively acceptable? The Cq method assumes that you have the same efficiency for all genes in all samples. If you can show that this assumption is reasonable it is normally acceptable to work with this method.

12 Q: TaqMan oligos and probes are supposed to achieve 100% efficiency in qpcr assays. Do you still perform standard curves when using TaqMan probes? The 100% efficiency is a theoretical value and the actual value you receive for your assay, reagents, samples etc. may vary largely. You have to verify what efficiency you have for your system. Q: I would like to know how long it takes to validate qrt-pcr reaction and to "get" proper efficiency of reaction? That depends completely on how difficult your system is and how extensive optimization and validation you need to perform. Q: (Additional question from the person analyzing pathogens in plants) thanks a lot for the answer, but it is still not clear because using GeNorm or Normfinder, both programs calculate the best ref gene based on constant expression of the gene within the samples, but due changes in the amount of plant or pathogen in each sample, the gene expression looks not constant because the amount of plant or pathogen is different. I would like to know the pathogen gene expression (using as a ref gene a pathogen gene) and also plant pathogen gene expression (using as a ref a plant gene). Neither GeNorm nor Normfinder assumes the same amount of material in each sample to be able to perform the analysis. They are in different ways analyzing how the expression of the different genes relative to each other varies from sample to sample, not the absolute values. But you still need to analyze for the plant genes and pathogen genes separately. Q: Is it possible to use double normalization, 1 to normalize for tissue type (when sample contains mixed tissue types) and second to normalize to ref genes? It is quite common to normalize on different levels. For example, you normally try to start with the same amount of material, then you do a total RNA normalization before the RT-step and finally you normalize with reference genes. However, it is important to remember that if you do this way the only normalization you actually use for the calculation is the reference genes, the other normalizations are just for your quality control to make sure that every step has worked properly. Q: We observe that the exponential growth of the PCR curve often does not correlate as expected with assay efficiency. Ie a steeper curve in the exponential phase doesn't necessarily mean that the assay has high efficiency (from standard curves ). Have you observed the same? And if so, can you explain the reason for this? Yes we see that too sometimes. You have to remember that the part of the amplification that we can visualize as the amplification curve is the part where we have accumulated enough material to be above the background signal which is rather close to where we already start to fall out of the exponential phase to get very obvious inhibition of the amplification. What happens during the cycled before that we do not really know and this is also one of the reasons why I am not that fond of using the amplification curve to measure the efficiencies.